Senior Research Scientist in Battery Materials Simulation
The Batteries vertical sits at the intersection of ChemSim and AI Sim, where SandboxAQ uses physics-based simulation, proprietary datasets, and Large Quantitative Models (LQMs) to discover and optimize next-generation battery materials.Our goal is to compress slow, empirical battery R&D into an AI-driven workflow spanning prediction, simulation, and materials discovery for high-impact applications including solid-state batteries, Cobalt-free cathodes, beyond Li-ion cell chemistries, and resilient energy storage systems.Weare seeking a highly skilled Senior Research Scientist in Battery Materials Simulation to join our growing team.
The ideal candidate will have deep expertise in applying advanced simulation techniques, including Density Functional Theory (DFT), Molecular Dynamics (MD), and machine learning (ML), to battery materials discovery and optimization.
This role will focus on developing computational workflows and AI-driven approaches to accelerate the design of next-generation battery materials, including cathodes, anodes, electrolytes, interfaces, and interphases. Experience modeling surface chemistry, interfacial degradation mechanisms, and electrochemical reaction pathways is highly desirable.
As a senior member of the team, the candidate will provide technical leadership, mentor junior scientists, and drive the execution of strategic research programs in collaboration with internal and external partners.
Key Responsibilities
- Conduct advanced simulations using DFT, MD, enhanced sampling methods, and ML-based approaches for battery materials and electrochemical systems.
- Model surface reactions, interfacial degradation mechanisms, and electrochemical processes, including cathode-electrolyte interfaces (CEI), solid-electrolyte interphases (SEI), solid-state electrolyte interfaces, and reaction pathways under operating conditions.
- Develop and deploy computational workflows for high-throughput materials screening, reaction modeling, and materials optimization.
- Lead high-fidelity data generation campaigns and develop ML force fields and surrogate models for battery materials and interfaces.
- Employ data-driven approaches to analyze large computational and experimental datasets to uncover new insights into materials behavior.
- Guide project scoping, execution, and delivery while working closely with cross-functional teams.
- Provide technical direction for battery research roadmaps, translate high-level project goals into technical milestones, and mentor junior scientists in best practices for both ML and physics-based modeling.
- Collaborate with internal teams, academic collaborators, government partners, and industrial customers to deliver impactful materials innovation.
- Effectively communicate research findings through scientific publications, conference presentations, client-facing presentations, and technical reports.
Essential Skills & Experiences
- Ph.D. in Materials Science, Chemical Engineering, Chemistry, Physics, Computer Science, or a related field.
- 5+ years of industry experience in computational battery materials research beyond the Ph.D.
- Strong theoretical foundation in thermodynamics, kinetics, electrochemistry, and materials science.
- Proficiency in DFT and atomistic simulation tools (e.g., VASP, Quantum ESPRESSO, CP2K).
- Familiarity with state-of-the-art machine learning force fields and frameworks (e.g. MACE, TensorNet, NequIP, Allegro, or FairChem).
- Experience modeling surfaces, interfaces, reaction pathways, and electrochemical systems.
- Experience training and evaluating ML models for materials property prediction and materials discovery.
- Experience with Bayesian optimization, active learning, and autonomous discovery workflows.
- Strong programming skills in Python and experience with modern ML frameworks (e.g., PyTorch, TensorFlow, JAX).
- Experience working with cloud and high-performance computing environments.
- Demonstrated ability to independently drive complex technical projects.
- Excellent communication and collaboration skills.
Highly Desired Skills & Experience
- Extensive experience modeling battery materials, including cathodes, anodes, liquid electrolytes, solid-state electrolytes, and electrochemical interfaces.
- Extensive background modeling rare-event phenomena, charge-transfer kinetics, or degradation at the solid-electrolyte interphase (SEI).
- Experience developing and deploying ML force fields for battery materials and reactive systems.
- Track record of developing or scaling generative models for materials synthesis, crystal structure generation, or unconstrained composition exploration.
- Track record of publications in high-impact peer-reviewed journals and/or patents in battery materials, computational chemistry, or AI for materials science.
- Experience leading technical programs and mentoring scientists in an industrial or national laboratory setting.
- Experience collaborating with experimental teams to validate computational predictions and accelerate materials development.
- Demonstrated success in translating computational discoveries into real-world materials innovation.
Why Join Us?
We offer competitive compensation, a comprehensive benefits package, and opportunities for professional growth.
- Compensation: Competitive base salary commensurate with experience, plus equity and performance-based incentives.
- Benefits: Comprehensive health, dental, and vision insurance; 401(k) with company match; generous parental leave.
- Work-Life Balance: Flexible hybrid work arrangements, generous PTO, and a culture that respects focus time and recovery.
- Career Development: Direct exposure to CHIPS Act-funded programs, senior scientific and executive leadership, mentorship, and dedicated learning budgets to support continued growth.
SandboxAQ Welcomes All
We are committed to fostering a culture of belonging and respect, where diverse perspectives are actively sought and valued. Our multidisciplinary environment provides ample opportunity for continuous growth - working alongside humble, empowered, and ambitious colleagues ready to tackle epic challenges.
Equal Employment Opportunity: All qualified applicants will receive consideration regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status.
Accommodations: We provide reasonable accommodations for individuals with disabilities in job application procedures for open roles. If you need such an accommodation, please let a member of our Recruiting team know.
Read: Guidance for candidates on using AI Tools in interviews
Senior Research Scientist in Battery Materials Simulation
The Batteries vertical sits at the intersection of ChemSim and AI Sim, where SandboxAQ uses physics-based simulation, proprietary datasets, and Large Quantitative Models (LQMs) to discover and optimize next-generation battery materials.Our goal is to compress slow, empirical battery R&D into an AI-driven workflow spanning prediction, simulation, and materials discovery for high-impact applications including solid-state batteries, Cobalt-free cathodes, beyond Li-ion cell chemistries, and resilient energy storage systems.Weare seeking a highly skilled Senior Research Scientist in Battery Materials Simulation to join our growing team.
The ideal candidate will have deep expertise in applying advanced simulation techniques, including Density Functional Theory (DFT), Molecular Dynamics (MD), and machine learning (ML), to battery materials discovery and optimization.
This role will focus on developing computational workflows and AI-driven approaches to accelerate the design of next-generation battery materials, including cathodes, anodes, electrolytes, interfaces, and interphases. Experience modeling surface chemistry, interfacial degradation mechanisms, and electrochemical reaction pathways is highly desirable.
As a senior member of the team, the candidate will provide technical leadership, mentor junior scientists, and drive the execution of strategic research programs in collaboration with internal and external partners.
Key Responsibilities
- Conduct advanced simulations using DFT, MD, enhanced sampling methods, and ML-based approaches for battery materials and electrochemical systems.
- Model surface reactions, interfacial degradation mechanisms, and electrochemical processes, including cathode-electrolyte interfaces (CEI), solid-electrolyte interphases (SEI), solid-state electrolyte interfaces, and reaction pathways under operating conditions.
- Develop and deploy computational workflows for high-throughput materials screening, reaction modeling, and materials optimization.
- Lead high-fidelity data generation campaigns and develop ML force fields and surrogate models for battery materials and interfaces.
- Employ data-driven approaches to analyze large computational and experimental datasets to uncover new insights into materials behavior.
- Guide project scoping, execution, and delivery while working closely with cross-functional teams.
- Provide technical direction for battery research roadmaps, translate high-level project goals into technical milestones, and mentor junior scientists in best practices for both ML and physics-based modeling.
- Collaborate with internal teams, academic collaborators, government partners, and industrial customers to deliver impactful materials innovation.
- Effectively communicate research findings through scientific publications, conference presentations, client-facing presentations, and technical reports.
Essential Skills & Experiences
- Ph.D. in Materials Science, Chemical Engineering, Chemistry, Physics, Computer Science, or a related field.
- 5+ years of industry experience in computational battery materials research beyond the Ph.D.
- Strong theoretical foundation in thermodynamics, kinetics, electrochemistry, and materials science.
- Proficiency in DFT and atomistic simulation tools (e.g., VASP, Quantum ESPRESSO, CP2K).
- Familiarity with state-of-the-art machine learning force fields and frameworks (e.g. MACE, TensorNet, NequIP, Allegro, or FairChem).
- Experience modeling surfaces, interfaces, reaction pathways, and electrochemical systems.
- Experience training and evaluating ML models for materials property prediction and materials discovery.
- Experience with Bayesian optimization, active learning, and autonomous discovery workflows.
- Strong programming skills in Python and experience with modern ML frameworks (e.g., PyTorch, TensorFlow, JAX).
- Experience working with cloud and high-performance computing environments.
- Demonstrated ability to independently drive complex technical projects.
- Excellent communication and collaboration skills.
Highly Desired Skills & Experience
- Extensive experience modeling battery materials, including cathodes, anodes, liquid electrolytes, solid-state electrolytes, and electrochemical interfaces.
- Extensive background modeling rare-event phenomena, charge-transfer kinetics, or degradation at the solid-electrolyte interphase (SEI).
- Experience developing and deploying ML force fields for battery materials and reactive systems.
- Track record of developing or scaling generative models for materials synthesis, crystal structure generation, or unconstrained composition exploration.
- Track record of publications in high-impact peer-reviewed journals and/or patents in battery materials, computational chemistry, or AI for materials science.
- Experience leading technical programs and mentoring scientists in an industrial or national laboratory setting.
- Experience collaborating with experimental teams to validate computational predictions and accelerate materials development.
- Demonstrated success in translating computational discoveries into real-world materials innovation.
Why Join Us?
We offer competitive compensation, a comprehensive benefits package, and opportunities for professional growth.
- Compensation: Competitive base salary commensurate with experience, plus equity and performance-based incentives.
- Benefits: Comprehensive health, dental, and vision insurance; 401(k) with company match; generous parental leave.
- Work-Life Balance: Flexible hybrid work arrangements, generous PTO, and a culture that respects focus time and recovery.
- Career Development: Direct exposure to CHIPS Act-funded programs, senior scientific and executive leadership, mentorship, and dedicated learning budgets to support continued growth.
SandboxAQ Welcomes All
We are committed to fostering a culture of belonging and respect, where diverse perspectives are actively sought and valued. Our multidisciplinary environment provides ample opportunity for continuous growth - working alongside humble, empowered, and ambitious colleagues ready to tackle epic challenges.
Equal Employment Opportunity: All qualified applicants will receive consideration regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status.
Accommodations: We provide reasonable accommodations for individuals with disabilities in job application procedures for open roles. If you need such an accommodation, please let a member of our Recruiting team know.
Read: Guidance for candidates on using AI Tools in interviews
